A pretrained star types classifier that sorts an image into one of 10 categories — what type of star it is. Use the star types API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 16 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Black Hole",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 star types categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
Scientists can employ the 'star types' identifier to automatically classify and catalog images of celestial bodies collected from telescopes. This can accelerate research efforts by allowing astronomers to focus on analyzing the data rather than manually categorizing images.
Educational institutions can use this function as a teaching aid in astronomy classes. Students can engage with interactive lessons that analyze star images, helping them understand different star types and their characteristics through practical application.
Developers can integrate the star types identifier into mobile applications focused on stargazing or astronomy. Users can take photos of the night sky, and the app will classify and provide information about the visible stars, enhancing the amateur astronomer experience.
Businesses that deal with image databases can utilize the classification function for better tagging and retrieval. By automating the organization of star images, users can efficiently search for specific types of stars or related data, ultimately improving data management.
Companies producing telescope imaging software can include this function to offer enhanced features. By allowing users to automatically classify and receive detailed insights about the stars their telescope captures, the user experience is significantly enriched.
Artists or creative agencies can use the identifier to inspire space-themed projects, installations, or artworks. The classification data can inform creative decisions, helping artists accurately represent different star types in their works.
Research organizations conducting large astronomical surveys can implement the star types identifier to process vast amounts of image data quickly. This automation allows for more efficient data analysis, leading to faster discoveries about star populations and their distributions in the universe.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This star types classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.